If you can’t measure it, you can’t improve it.

You start first with imagination. Think about what it means to step inside the mind of your employee and see/feel/experience what they are going through, what questions they are asking themselves and what issues they need resolved in order to be happy at work. Then imagine being in a position to address this, without them realising what you’re doing.


How would this improve the employee experience? In what way could you reimagine employee feedback being obtained? How could you provide unified yet automated employee support? The stuff of dreams? Not anymore.


We’re talking about the world of data analytics – quantitative and qualitative techniques and processes that can ultimately enhance productivity and improve the business. It involves extracting data, categorising it and then analysing it for patterns. So if you’re working with data, you need to perform analytics at some point.


Big Data, as a term, has been bandied about for some time although the concept is still very new to those in HR. The area it seems to have made most impact is talent acquisition where companies have handled thousands of resumes every year, using machine learning (producing faster and more accurate hiring decisions than humans alone).


What does analytics look like applied to HR?


Like many other areas to which analytics applies, it’s really about figuring out how to solve problems. In HR, this means considering how you could use data and analytics to:

  • improve the employee experience;
  • develop talent;
  • automate employee support;
  • move into the Cloud;
  • improve how we receive employee feedback;
  • take advantage of machine learning.


The more data you have, the more insight you can derive from it, assuming you can extract and process the data well.  Done right, it can mean many things to those in HR.


First, it can aid on-boarding and retention by helping to develop a particular company culture and create a great work environment. Faced with high attrition costs and recruitment fees, doing this means you retain your employees longer.


Second, it can mean your ability to predict when employees are likely to resign (like in Facebook’s case), thus enabling you to take proactive steps to mitigate. Why rely on gut feel or wait until the situation implodes? Machine learning involves a “range of statistical techniques that allows companies to layout complex problems, spot patterns and come up with predictions”.


Third, it aids in talent acquisition where your company can go through thousands of resumes and create a shortlist of prospects.


Fourth, data analytics helps with one of the biggest issues in HR – performance and engagement.


How do I get my talent to perform at a higher level?
How do I see what’s preventing that from happening?
How do I begin to understand the issues on the periphery?
What can I do to bring the team closer together?
How can I make them all see one unified vision?

Big asks but that is exactly why it makes sense to use analytics in HR – so you don’t just guess your way through the problem. Instead, you allow yourself to be guided forward with facts and figures.


In this way, SelfDrvn uses data-driven solutions to provide valuable insights through a range of touchpoints including employee pulse surveys, reward and recognition programmes, peer feedback loops, goal-setting, wellness games and leaderboard competitions. It’s easy to think of HR analytics as purely the responsibility of HR but it is not. Application and analysis ultimately benefit the organisation as a whole.